Multimorbidity (MM), conventionally defined as two or more chronic diseases, is a public health problem in older adults. MM patients are at high risk of iatrogenesis from polypharmacy, disability, and premature death, with challenges to caregivers and cost to society. The study of the pathophysiology of MM based on single biomarkers or pathways has had limited success. Moreover, there are no comprehensive biomarkers developed to evaluate and predict MM. Previous experience and preliminary data corroborate the hypothesis of the existence of potential DNA methylation (DNAm) biomarkers that are associated with MM at the population level. Additionally, the study of mechanisms of MM at the tissue level in humans have been limited due to ethical and logistical complications. Neglected yet unarguably pivotal, identifying DNAm markers associated with MM mouse models is of paramount importance to develop our capabilities to predict MM and shed more light on its underlying mechanisms. The overarching goal of this proposal is to identify DNAm markers of MM in human populations and mouse models to develop a set of comprehensive tools for the identification of at-risk individuals. The resulting markers will be applicable to both male and female individuals. Using four population cohorts, I will test the use of DNAm markers to predict who is likely develop MM. I will measure how cell specific DNAm and gene expression (RNA-seq) are related and different between individuals who are free of MM at baseline and developed MM and those who remained free of MM. I will use an animal model of MM using histopathology studies of organs to define mm and measure DNAm and cognate transcriptomics (RNA-seq). The results of this study can be used to better identify and understand the shared mechanism(s) that causes MM. Collectively, identifying ?DNA methylation markers of multimorbidity in aging humans and mice? should allow geriatricians to predict multimorbidity in older adults and help researchers to further understand the mechanisms underlying multimorbidity. The results of this project, in the future, can be applied to test the effectiveness of interventions such as metformin that target these mechanisms for prevention and treatment of MM.